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Assessing the validity of autologistic regression

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  • Dormann, Carsten F.

Abstract

In autologistic regression models employed in the analysis of species’ spatial distributions, an additional explanatory variable, the autocovariate, is used to correct the effect of spatial autocorrelation. The values of the autocovariate depend on the values of the response variable in the neighbourhood. While this approach has been widely used over the last ten years in biogeographical analyses, it has not been assessed for its validity and performance against artificial simulation data with known properties. I here present such an assessment, varying the range and strength of spatial autocorrelation in the data as well as the prevalence of the focal species. Autologistic regression models consistently underestimate the effect of the environmental variable in the model and give biased estimates compared to a non-spatial logistic regression. A comparison with other methods available for the correction of spatial autocorrelation shows that autologistic regression is more biased and less reliable and hence should be used only in concert with other reference methods.

Suggested Citation

  • Dormann, Carsten F., 2007. "Assessing the validity of autologistic regression," Ecological Modelling, Elsevier, vol. 207(2), pages 234-242.
    • Handle: RePEc:eee:ecomod:v:207:y:2007:i:2:p:234-242
    DOI: 10.1016/j.ecolmodel.2007.05.002
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    1. Arbia, Giuseppe & Benedetti, Roberto & Espa, Giuseppe, 1999. "Contextual classification in image analysis: an assessment of accuracy of ICM," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 443-455, June.
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    3. Carl, G. & Kühn, I., 2007. "Analyzing spatial autocorrelation in species distributions using Gaussian and logit models," Ecological Modelling, Elsevier, vol. 207(2), pages 159-170.
    4. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
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